Analisis Operator Filter Second Vertical Derivative (SVD) Pada Anomali Gayaberat Menggunakan Pemrograman Python

Abstract

This research discusses about Second Vertical Derivative (SVD) method using SVD Filter Operator which is applied to gravity anomaly data using Python Programming. The purpose of this study is to see the performance of each technique for calculating SVD anomaly values on Synthetic and Field Work Data using Python Programming. This final project research uses 5 synthetics models including the Synthetic One-Source Anomaly Model with variations in depth Shallow, Medium, Deep, Two-Source Anomaly Synthetic Model, Synthetic Model Two Overlap Anomaly Sources. Then we used Field Data at the Fallon Site, Nevada which was obtained from the Geothermal Data Repository (GDR). The work on this final project uses the software Jetbrains Pycharm for the preparation of scripts programming. The 2D matrix convolution method used by superimposing Kernel Window containing the multiplier numbers for each pixel that is overwritten, then the sum value is taken from the results of the multiplication, the Kernel here is the SVD Filter Operator used in this final project, including Operators Henderson & Ziets (1949), Operators Elkins (1951), and Operators Rosenbach (1953) which will later carry out the 2D matrix convolution process with gravity anomaly grid data. Then the results obtained on synthetic data that each operator can delineate the existing anomalous edges but will be optimal only on shallow structured data or high frequency, so that gravity anomaly with deep depth variations will not be optimal due to the influence of regional effects from the anomalies formed, then the results of the SVD The optimum is obtained from the Elkins Operator (1951) it shows in every synthetic data used that the Elkins Operator (1951) has the smallest and most representative relative anomaly shift value. Furthermore, the analysis was carried out on the Fallon Nevada Site Field Data. The most optimal results were obtained on the Elkins Operator (1951) because it was able to visualize the optimal results with best resolution results, the most representative anomaly boundary boundaries were obtained when compared to other operators.